论文标题
加权编码优化用于动态单金成像和传感
Weighted Encoding Optimization for Dynamic Single-pixel Imaging and Sensing
论文作者
论文摘要
使用单像素检测，共同优化编码和解码的端到端神经网络可实现高精度成像和高级语义感测。但是，对于不同的采样率，大规模网络需要劳动和计算消费的重新培训。在这封信中，我们报告了一种加权优化技术，用于动态速率自适应单像素成像和传感，该技术只需要一次训练网络一次，用于任何采样率。具体而言，我们在编码过程中介绍了一种新颖的加权方案，以表征不同模式的调制效率。当网络以高采样率训练时，调制模式和相应的权重迭代更新，在收敛时会产生最佳的排名编码系列。在实验实现中，使用具有最高权重的最佳模式序列用于光调制，从而实现了高效的成像和传感。报告的策略节省了现有动态单像素网络要求的另一个低速网络的额外培训，这进一步使训练效率增加了一倍。 MNIST数据集的实验验证了一旦对网络的采样率为1进行训练，平均成像PSNR以0.1采样率以0.1的采样率达到23.50 dB，并且以0.03和97.91 \％的采样率，以0.03和97.91 \％的采样率达到95.00 \％。
Using single-pixel detection, the end-to-end neural network that jointly optimizes both encoding and decoding enables high-precision imaging and high-level semantic sensing. However, for varied sampling rates, the large-scale network requires retraining that is laboursome and computation-consuming. In this letter, we report a weighted optimization technique for dynamic rate-adaptive single-pixel imaging and sensing, which only needs to train the network for one time that is available for any sampling rates. Specifically, we introduce a novel weighting scheme in the encoding process to characterize different patterns' modulation efficiency. While the network is training at a high sampling rate, the modulation patterns and corresponding weights are updated iteratively, which produces optimal ranked encoding series when converged. In the experimental implementation, the optimal pattern series with the highest weights are employed for light modulation, thus achieving highly-efficient imaging and sensing. The reported strategy saves the additional training of another low-rate network required by the existing dynamic single-pixel networks, which further doubles training efficiency. Experiments on the MNIST dataset validated that once the network is trained with a sampling rate of 1, the average imaging PSNR reaches 23.50 dB at 0.1 sampling rate, and the image-free classification accuracy reaches up to 95.00\% at a sampling rate of 0.03 and 97.91\% at a sampling rate of 0.1.
